"""simple docstring"""



from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE__:Tuple									=			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def 	_lowerCamelCase(	a				):
       if isinstance(a   ,    torch.Tensor				):
              return image
       elif isinstance(a   ,    PIL.Image.Image				):
              __a       =    [image]

       __a       =    [trans(img.convert("RGB"				)				) for img in image]
       __a       =    torch.stack(a				)
       return image






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        super().__init__()

        # make sure scheduler can always be converted to DDIM
        __a       =    DDIMScheduler.from_config(scheduler.config						)

        self.register_modules(unet=lowerCamelCase    ,		scheduler=lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        if strength < 0 or strength > 1:
               raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        # get the original timestep using init_timestep
        __a       =    min(int(num_inference_steps * strength						)    ,		lowerCamelCase						)

        __a       =    max(num_inference_steps - init_timestep    ,		0						)
        __a       =    self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase=None						):
        if not isinstance(lowerCamelCase    ,		(torch.Tensor, PIL.Image.Image, list)						):
               raise ValueError(
                   F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase						)}"						)

        __a       =    image.to(device=lowerCamelCase    ,		dtype=lowerCamelCase						)

        if isinstance(lowerCamelCase    ,		lowerCamelCase						) and len(lowerCamelCase						) != batch_size:
               raise ValueError(
                   F"You have passed a list of generators of length {len(lowerCamelCase						)}, but requested an effective batch"
                   F" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

        __a       =    init_latents.shape
        __a       =    randn_tensor(lowerCamelCase    ,		generator=lowerCamelCase    ,		device=lowerCamelCase    ,		dtype=lowerCamelCase						)

        # get latents
        print("add noise to latents at timestep"    ,		lowerCamelCase						)
        __a       =    self.scheduler.add_noise(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    init_latents

        return latents






 @torch.no_grad()
 def __call__( self    ,		lowerCamelCase = None    ,		lowerCamelCase = 0.8    ,		lowerCamelCase = 1    ,		lowerCamelCase = None    ,		lowerCamelCase = 0.0    ,		lowerCamelCase = 50    ,		lowerCamelCase = None    ,		lowerCamelCase = "pil"    ,		lowerCamelCase = True    ,		):
        self.check_inputs(lowerCamelCase						)

        # 2. Preprocess image
        __a       =    preprocess(lowerCamelCase						)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowerCamelCase    ,		device=self.device						)
        __a ,   __a       =    self.get_timesteps(lowerCamelCase    ,		lowerCamelCase    ,		self.device						)
        __a       =    timesteps[:1].repeat(lowerCamelCase						)

        # 4. Prepare latent variables
        __a       =    self.prepare_latents(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		self.unet.dtype    ,		self.device    ,		lowerCamelCase						)
        __a       =    latents

        # 5. Denoising loop
        for t in self.progress_bar(lowerCamelCase						):
               # 1. predict noise model_output
               __a       =    self.unet(lowerCamelCase    ,		lowerCamelCase						).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __a       =    self.scheduler.step(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		eta=lowerCamelCase    ,		use_clipped_model_output=lowerCamelCase    ,		generator=lowerCamelCase    ,		).prev_sample

        __a       =    (image / 2 + 0.5).clamp(0    ,		1						)
        __a       =    image.cpu().permute(0    ,		2    ,		3    ,		1						).numpy()
        if output_type == "pil":
               __a       =    self.numpy_to_pil(lowerCamelCase						)

        if not return_dict:
               return (image, latent_timestep.item())

        return ImagePipelineOutput(images=lowerCamelCase						)

"""simple docstring"""



import os



def 	_lowerCamelCase(	a = "matrix.txt"				):
       with open(os.path.join(os.path.dirname(a				)   ,    a				)				) as in_file:
              __a       =    in_file.read()

       __a       =    [[int(a				) for cell in row.split(","				)] for row in data.strip().splitlines()]
       __a       =    [[0 for cell in row] for row in grid]
       __a       =    len(grid[0]				)

       __a       =    [[0 for i in range(a				)] for j in range(a				)]
       __a       =    grid[0][0]
       for i in range(1   ,    a				):
              __a       =    grid[0][i] + dp[0][i - 1]
       for i in range(1   ,    a				):
              __a       =    grid[i][0] + dp[i - 1][0]

       for i in range(1   ,    a				):
              for j in range(1   ,    a				):
                     __a       =    grid[i][j] + min(dp[i - 1][j]   ,    dp[i][j - 1]				)

       return dp[-1][-1]


if __name__ == "__main__":
   print(F'''{solution() = }''')
